A Large-Scale Community Questions Classification Accounting for Category Similarity: An Exploratory?

Abstract

The paper reports on a large-scale topical categorization of questions from a Russian community question answering (CQA) service Otvety@Mail.Ru. We used a data set containing all the questions (more than 11 millions) asked by Otvety@Mail.Ru users in 2012. This is the first study on question categorization dealing with non-English data of this size. The study focuses on adjusting category structure in order to get more robust classification results. We investigate several approaches to measure similarity between categories: the share of identical questions, language models, and user activity. The results show that the proposed approach is promising.